In recent years, as the use of smart devices and the amount of demand data required for each device increase, a plurality of wireless LAN (Local Area Network) APs (Access Points) have been indiscriminately installed in order to solve the aforementioned issues. This results in that interference between APs disposed in adjacent cells becomes larger. The interference between adjacent cells causes the deterioration of system performance and an added difficulty in wireless LAN connection. An interference alignment has been proposed. The interference alignment refers to a technique to align interference signals to specific resources, e.g., such as time, space, frequency, etc. to minimize the interference so that a desired signal can be correctly sent to a desired reception end.
For example, when the interference alignment is done using multiple antennas in a wireless LAN environment, interference signals reached from different APs is assigned to specific spatial resources during signals are received at a station so as to secure as much as possible a space through which a desired signal can be sent, thereby making it easy to separate the desired signal from the interference signals. It is possible to obtain maximum performance if all of the users within the interference channel environment can take advantage of DoF (Degrees of Freedom) by up to half an antenna resource using the interference alignment technique.
The term of DoF used herein refers to a maximum number of streams that can transmit a signal without any interference. As such, this interference alignment technique has attracted a lot of attention in terms of being able to solve the problem of interference between adjacent cells. However, the interference alignment has several disadvantages in that a complex calculation is required when obtaining precoding/decoding signal processing filters that are used in transmission and reception ends, each node needs to know the large amount of radio channel condition information, and the number of antennas should be more than enough to make null the interference aligned in proportion to the number of interference sources.
In view of properties of algorithm of the existing interference alignment, it can be seen that it is difficult to apply the interference alignment algorithm to a real system. Basically, in order for applying the interference alignment algorithm to the real system, it is necessary to linearly apply the transmission and reception signal processing filters to the real system in order to maximize the time during which real data is transmitted. In some of the existing algorithms, there may be required the process of repetitively exchanging communications between the transmission and reception ends, which may result in excessively increased overhead when being applied to the real system.
Secondly, in the conventional algorithm, all of transmission and reception nodes need to know Global Channel State Information. However, the overhead may be introduced because each node needs to know even information about channels not related thereto the nodes. Therefore, it may cause degradation of system performance.
Thirdly, the transmitting node and the reception node may need to cooperate with each other when they perform the precoding/decoding processes. In this case, it is necessary a host controller that presents at upper stage or information sharing between the nodes intended to the cooperation, which makes it difficult to apply the interference alignment algorithm to the real system.
Finally, it is required that the interference alignment algorithm should be applied even any number of nodes. Basically, in an existing linear algorithm, there are many cases where the algorithm is applied to only a specific number of nodes. It is therefore hard to preemptively estimate the number of nodes which will participate in the interference alignment owing to the influence of increased interference. Accordingly, there is a need for the development of an algorithm capable of applying even a common number of nodes.